Automatic license plate recognition is important for the management of vehicle flow in an intelligent traffic infrastructure. Historically, tolling has occurred at tollbooths where immediate payment would occur as vehicles proceeded down the lane past the booths. As automated payment methods and vehicle identification methods become available, these booths are removed and the lanes are converted to electronic tolling, also known as open road tolling (ORT).
In standard tollbooth based tolling scenarios, images of vehicle license plates are taken under controlled conditions. The tollbooth is typically covered and well illuminated which enables acquisition of uniformly illuminated plate images. However, as the adoption of electronic tolling has become more widespread, the need for cars to slow down to pass through a tollbooth is seen as an unnecessary impediment to the free flow of traffic. Therefore, there continues to be a conversion to open road tolling. In open road tolling, the cars are generally monitored by cameras attached to a gantry above the highway where the traffic flows past at full speed.
As a result, for open road tolling, conditions under which images of cars are collected are less controlled. One of the challenges is the appearance of shadows cast onto the plates. For many vehicles, the plates are recessed behind a structure on the bumper that contains lights to illuminate the plate during darkness. Depending on the angle of the sun, during the day a shadow may be cast onto the license plate from the vehicle structure and/or the gantry. This leads to an image where a part of the plate is considerably darker than the rest.
For some plate recognition algorithms, partial shading of license plates leads to poor recognition performance. Typically a binarization step is used as preprocessing in optical character recognition algorithms. After binarization, the shaded area may be identified as part of the character. Alternatively, the character regions of the unshaded areas may be washed out and be chosen as the background.
The primary need that has driven shadow detection in other video based applications is tasks such as object detection, segmentation, and tracking. In such cases the shadows move along with the object across multiple frames and tend to be identified as part of the foreground. The vision algorithms for object detection and tracking have been primarily developed for natural scenes and require temporal information from multiple frames.
By contrast, the detection of shadows cast on a license plate or on text has received basically no attention. The applications are indeed limited because the application is specifically for optical character recognition (OCR). In many cases, OCR on printed documents is performed on images captured under controlled conditions where shadows would not be present.
There is, therefore, a need for effective shadow detection and elimination for application in license plate detection and OCR applications as described herein.